Discrimination of the H1N1 and H5N2 Variants of Influenza A Virus Using an Isomeric Sialic Acid-Conjugated Graphene Field-Effect Transistor.

There has been a continuous effort to fabricate a fast, sensitive, and inexpensive system for influenza virus detection to meet the demand for effective screening in point-of-care testing. Herein, we report a sialic acid (SA)-conjugated graphene field-effect transistor (SA-GFET) sensor designed using α2,3-linked sialic acid (3'-SA) and α2,6-linked sialic acid (6'-SA) for the detection and discrimination of the hemagglutinin (HA) protein of the H5N2 and H1N1 viruses. 3'-SA and 6'-SA specific for H5 and H1 influenza were used in the SA-GFET to capture the HA protein of the influenza virus. The net charge of the captured viral sample led to a change in the electrical current of the SA-GFET platform, which could be correlated to the concentration of the viral sample. This SA-GFET platform exhibited a highly sensitive response in the range of 101-106 pfu mL-1, with a limit of detection (LOD) of 101 pfu mL-1 in buffer solution and a response time of approximately 10 s. The selectivity of the SA-GFET platform for the H1N1 and H5N2 influenza viruses was verified by testing analogous respiratory viruses, i.e., influenza B and the spike protein of SARS-CoV-2 and MERS-CoV, on the SA-GFET. Overall, the results demonstrate that the developed dual-channel SA-GFET platform can potentially serve as a highly efficient and sensitive sensing platform for the rapid detection of infectious diseases.

Salivary biomarkers: The early diagnosis of Alzheimer's disease.

The precise identification of Alzheimer's disease and other prevalent neurodegenerative diseases remains a difficult issue that requires the development of early detection of the disease and inexpensive biomarkers that can replace the present cerebrospinal fluid and imaging biomarkers. Blood biomarkers, such as amyloid and neurofilament light, have been emphasized as an important and practical tool in a testing or examination procedure thanks to advancements in ultra-sensitive detection techniques. Although saliva is not currently being researched for neurodegenerative diseases, it is an important source of biomarkers that can be used for the identification of diseases and has some advantages over other biofluids. While this may be true for most people, getting saliva from elderly people presents some significant challenges. In this overview, we will first discuss how saliva is created and how aging-related illnesses may affect the amount and kind of saliva produced. The findings support the use of salivary amyloid protein, tau species, and novel biomarkers in the diagnosis of Alzheimer's disease.

Recent Progress in the Application of Tau Protein Biosensors for Diagnosis of Neurodegenerative Diseases.

The microtubule-associated Tau protein is found in the central nervous system (CNS) in six major isoforms. Neurodegenerative diseases have been linked to post-translational changes of Tau, most notably phosphorylation. Tau protein's molecular diversity is highly helpful in the identification of neurodegenerative illnesses. Nonetheless, one major obstacle to the early detection of brain illness is the nanoscale identification of tau proteins. The standard methods for identifying tau protein include western blotting, polymerase chain reaction (PCR), and real-time PCR. Enzyme-linked immunosorbent assay (ELISA) is another approach used. The limited sensitivity and specificity of these detections, together with the need for sophisticated equipment, are some of their drawbacks. The development of innovative and complex methods for tau protein screening is necessary to address the aforementioned issues. Biosensors are a cutting-edge instrument that may help identify various neurodegenerative biomarkers as early as feasible. This paper provides an overview of the most recent developments in the detection of neurodegenerative diseases employing biosensors built on nanotechnology and methods for imaging, electrochemical, and optical detection of the Tau protein. Furthermore, we outline the present difficulties and suggest a possible course for biosensor-based detection and intervention in the future.